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Reliability, IEEE Transactions on

Issue 3 • Date Sept. 2009

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Displaying Results 1 - 24 of 24
  • Table of contents

    Page(s): C1 - 413
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  • IEEE Transactions on Reliability publication information

    Page(s): C2
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  • Publication Guidelines for IEEE Transactions on Reliability, and Perhaps Everyone

    Page(s): 414 - 415
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  • Discriminating Among the Log-Normal, Weibull, and Generalized Exponential Distributions

    Page(s): 416 - 424
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (343 KB) |  | HTML iconHTML  

    We consider model selection and discrimination among three important lifetime distributions. These three distributions have been used quite effectively to analyze lifetime data. We study the probability of correct selection using the maximized likelihood method, as it has been used in the literature. We further compute the asymptotic probability of correct selection, and compare the theoretical, and simulation results for different sample sizes, and for different model parameters. The results have been extended for Type-I censored data also. The theoretical, and simulation results match quite well. Two real data sets have been analyzed for illustrative purposes. We also suggest a method to determine the minimum sample size required to discriminate among the three distributions for a given probability of correct selection, and a user specified protection level. View full abstract»

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  • A Model for Upside-Down Bathtub-Shaped Mean Residual Life and Its Properties

    Page(s): 425 - 431
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    Mean residual life is an important statistic in reliability analysis. Based on a general functional form of the derivative of the mean residual life, we propose a new lifetime distribution with an upside-down bathtub-shaped mean residual life function. The model has its mean residual life function in a simple, closed form so that further analysis based on the mean residual life can be easily carried out. We study the analysis and applications on both the mean residual life function, and the failure rate function of this model. Maximum likelihood method is used for parameter estimation. Numerical examples and comparisons indicate that the new model performs well in modeling lifetime data with bathtub-shaped failure rate functions, and upside-down bathtub-shaped mean residual life function. View full abstract»

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  • Reliability Analysis of Mechanical Systems With Bounded and Bathtub Shaped Intensity Function

    Page(s): 432 - 443
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (446 KB) |  | HTML iconHTML  

    This paper presents a new stochastic point process able to analyse the failure pattern of complex mechanical systems experiencing both early failures and degradation phenomena, and operating so long that the intensity function approaches a finite asymptote as the system age grows. We illustrate the characteristics of the proposed model. Then we give the maximum likelihood estimators of the model parameters, and some quantities of interest such as the s-expected number of early failures, the time of minimum intensity, and the minimum intensity value. Inference on the number of failures that will occur in future time intervals is also provided. We applied the proposed model to real failure data from the powertrain system of two buses operating in urban routes in the city of Naples. View full abstract»

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  • Mis-Specification Analysis of Linear Degradation Models

    Page(s): 444 - 455
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    Degradation models are widely used to assess the lifetime information of highly reliable products if there exists quality characteristics whose degradation over time can be related to reliability. The performance of a degradation model depends strongly on the appropriateness of the model describing a product's degradation path. In this paper, motivated by laser data, we propose a general linear degradation path in which the unit-to-unit variation of all test units can be considered simultaneously with the time-dependent structure in degradation paths. Based on the proposed degradation model, we first derive an implicit expression of a product's lifetime distribution, and its corresponding mean-time-to-failure (MTTF). By using the profile likelihood approach, maximum likelihood estimation of parameters, a product's MTTF, and their confidence intervals can be obtained easily. In addition, laser degradation data are used to illustrate the proposed procedure. Furthermore, we also address the effects of model mis-specification on the prediction of the product's MTTF. It shows that the effect of the model mis-specification on the predictions of a product's MTTF is not critical under the case of large samples. However, when the sample size and the termination time are not large enough, a simulation study shows that these effects are not negligible. View full abstract»

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  • A New Discretization Approach With Application in Reliability Estimation

    Page(s): 456 - 461
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (199 KB) |  | HTML iconHTML  

    This paper introduces a new discretization technique that retains the basic structure of the failure rate function of the original life distribution. Earlier approaches for discretizing a continuous random variable, reported in the literature so far, are moment equalization, and discrete concentration techniques. This proposed approach has been used for approximating the reliability of complex systems where exact determination of survival probability is analytically intractable. These applications show a reasonable degree of closeness between this new method and the simulated results. Admissibility of the proposed method over the methods of discrete concentration and numerical integration has also been established. View full abstract»

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  • Selective Maintenance Decision-Making Over Extended Planning Horizons

    Page(s): 462 - 469
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (212 KB) |  | HTML iconHTML  

    ??Selective maintenance?? models determine the optimal subset of desirable maintenance actions to perform when maintenance resources are constrained. We analyse a corrective selective maintenance model that identifies which components to replace in the finitely long periods of time between missions performed by a series-parallel system. We formulate this multi-mission problem as a stochastic dynamic program, and compare the resulting optimal infinite-horizon policy to both the optimal single-mission, and two-mission policies by executing a large numerical experiment. Our results indicate that these policies rarely differ, and that when they do, the difference in long-run mission reliability is minimal, which suggests that future work should concentrate on extending results for the single-mission problem. View full abstract»

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  • Coverage Modeling and Optimal Maintenance Frequency of an Automated Restoration Mechanism

    Page(s): 470 - 475
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    In this paper, the coverage modeling of an automated restoration mechanism with two components is studied, and optimal maintenance frequencies are determined. Failures and repairs are exponentially distributed for the two components, while the failures of the automated mechanism follow a Weibull distribution. A corrective maintenance policy is considered for each component whereas, for the automated restoration mechanism, an additional preventive maintenance is taken into account. The system is modeled with a continuous time Markov chain and two performability indicators: one performability indicator modeling the downtime, and a second one modeling the overall operational cost. Consequently, two optimal maintenance frequencies are derived: first by minimizing the downtime, and second by minimizing the overall operational cost. A numerical example is used to evaluate the applicability of the proposed method. View full abstract»

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  • A Convolution Universal Generating Function Method for Evaluating the Symbolic One-to-All-Target-Subset Reliability Function of Acyclic Multi-State Information Networks

    Page(s): 476 - 484
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    The acyclic multi-state information network (AMIN) is an extension of the multi-state network without having to satisfy the flow conservation law. A very straightforward convolution universal generating function method (CUGFM) is developed to find the exact symbolic one-to-all-target-subset reliability function of AMIN. The correctness and computational complexity of the proposed algorithm will be proven. Two illustrative examples demonstrate the power of the proposed CUGFM to solve the exact symbolic reliability functions of the one-to-all-target-subset AMIN problem more efficiently than the best-known UGFM. View full abstract»

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  • Efficient Opportunistic Routing in Utility-Based Ad Hoc Networks

    Page(s): 485 - 495
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    Due to resource scarcity, a paramount concern in ad hoc networks is utilizing limited resources efficiently. The self-organized nature of ad hoc networks makes the network utility-based approach an efficient way to allocate limited resources. However, the effect of link instability has not yet been adequately addressed in literature. To efficiently address the routing problem in ad hoc networks, we integrate the cost and stability into a network utility metric, and adopt the metric to evaluate the routing optimality in a unified, opportunistic routing model. Based on this model, an efficient algorithm is designed, both centralized and distributed implementations are presented, and extensive simulations on NS-2 are conducted to verify our results. View full abstract»

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  • Comments on “A Class of Fault-Tolerant Multiprocessor Networks”

    Page(s): 496 - 500
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    A. Ghafoor presented node-disjoint paths of even networks using Figs. 4, 5, 6,and 7 (Ghafoor, IEEE Trans. Reliability, vol. 38, no. 1, pp. 5-15). However, the paper contains errors which cause confusion. We show that the node-disjoint paths, and Theorem 4 (Ghafoor, IEEE Trans. Reliability, vol. 38, no. 1, pp. 5-15), are not correct. We propose advanced node-disjoint paths, and prove that the fault diameter of even networks is d+1. This is optimal. View full abstract»

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  • Self-Organizing Migrating Strategies Applied to Reliability-Redundancy Optimization of Systems

    Page(s): 501 - 510
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    The reliability-redundancy allocation problem is a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, mixed-integer non-linear programming, heuristics, and meta-heuristics. Meanwhile, the development of meta-heuristics has been an active research area in optimizing system reliability wherein the redundancy, the component reliability, or both are to be determined. In recent years, a broad class of stochastic algorithms, such as simulated annealing, evolutionary computation, and swarm intelligence algorithms, has been developed for reliability-redundancy optimization of systems. Recently, a new class of stochastic optimization algorithm called SOMA (Self-Organizing Migrating Algorithm) has emerged. SOMA works on a population of potential solutions called specimen, and is based on the self-organizing behavior of groups of individuals in a "social environment". This paper introduces a modified SOMA approach based on a Gaussian operator to solve reliability-redundancy optimization problems. In this context, three examples of mixed integer programming in reliability-redundancy design problems are evaluated. In this application domain, SOMA was found to outperform the previously best-known solutions available. View full abstract»

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  • Optimal Design of Dependable Control System Architectures Using Temporal Sequences of Failures

    Page(s): 511 - 522
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (469 KB) |  | HTML iconHTML  

    Designing a dependable control system requires accurate methods to evaluate efficiently the dependability level of one given component architecture. This evaluation is crucial to determine the risks associated with system failures, and the remaining properties after fault occurrences. The dependability level of a control system depends not only on the kind of component failures that may occur, but also on the ordered sequences of the failure appearance. Classical evaluation methods, i.e. fault trees or failure mode and effect analysis, are not appropriate to handle these sequences. Our paper contributes on this aspect, and proposes a complete design methodology for dependable systems. This methodology uses ordered sequences of multiple failures to evaluate accurately the dependability level of all possible system's equipment architectures. Starting with the hierarchical functional decomposition of the system, the first step is to identify the dreaded events. Thus, the faulty behaviors of all possible system architectures are characterized with temporal operators. The set of system's operational architectures is finally determined by solving an optimization problem that considers both dependability objectives, and cost constraints. This methodology is applied to design a fire detection system for a railroad transportation system. In this paper, a complete methodology to design dependable control systems is presented. The innovative feature of this methodology is that it attempts to take into account time ordered sequences of failures. A new representation, called improved multi-fault tree, is defined. This tool allows us first to model failure relationships between functions, and second to evaluate the dependability level of a set of equipment architectures by the use of time ordered sequences of failures. Our design method provides a set of optimal architectures with given costs, and dependability levels. The designer can choose among these solutions trading amon- the costs, and dependability level specifications. The comparison between the new approach and the classical dependability method shows that the set of solutions for the multi-fault tree is smaller than the set of solutions for the classical one. The set is smaller, but the solutions are better because the new approach integrates temporal functions, and evaluates more precisely the level of dependability than with the traditional one. View full abstract»

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  • A New Real-Time Reliability Prediction Method for Dynamic Systems Based on On-Line Fault Prediction

    Page(s): 523 - 538
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    While a specific system is in use, its reliability will decrease gradually after the infant mortality period because of the components' degradation, or external attacks. Thus, reliability is a natural characteristic of a system's health, and can be used for condition monitoring & predictive maintenance. This paper introduces a new real-time reliability prediction method for dynamic systems which incorporates an on-line fault prediction algorithm. The factors that may reduce a system's reliability are modeled as an additive fault input to the system, and the fault is assumed to be varying linearly with time, approximately. The time-varying fault is roughly estimated based on a modified particle filtering algorithm at first. Then, as a time series, the fault estimate sequence is smoothed, and predicted by an exponential smoothing method. Mathematical analysis shows that the effects of the system, and measurement noises on the fault estimates are greatly reduced by exponential smoothing, which indicates that the comparatively high accuracy of the fault estimates & predictions is guaranteed. Based on the particle filtering & fault prediction results, the whole system's predictive reliability is computed through a Monte Carlo simulation strategy. The effectiveness of the proposed real-time reliability prediction method is validated by a computer simulation of a three-vessel water tank system. View full abstract»

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  • System Maintenance Scheduling With Prognostics Information Using Genetic Algorithm

    Page(s): 539 - 552
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    Condition based maintenance (CBM) aims to balance two extreme sides (i.e., corrective maintenance (CM), and preventive maintenance (PM)) by observing and forecasting the real time health of machines. Recent developments in CBM revealed promising technologies for advanced fault detection, and forecasting. Traditional maintenance scheduling in CBM is based on the threshold setting on forecasted failure probability, or remaining useful life (RUL) for individual components. However, this approach may not give the best result for the system, because individual components are inter-related, and mutually dependent. It is not uncommon in systems that turning off a machine due to failure or maintenance causes other machinery or components to be turned off. Designing a comprehensive tool that optimizes availability & cost of the whole system incorporating prognostics information is crucial to fully benefit from CBM. The goal of this paper is to emphasize this need by demonstrating scenarios in CM, PM, and CBM; and to present a solution that optimizes system availability, and cost with system-maintenance constraints using genetic algorithms. The proposed tool acquires the forecasted failure probability of individual components from the prognostics module, and their reliability expectations after maintenance. The tradeoff between maintenance & failure is quantified in risk as the objective function to be minimized. The risk is minimized utilizing genetic algorithms for the whole system rather than individual components. The results of the proposed tool are compared with PM, CM, and CBM in which prognostics information of components are analyzed individually. View full abstract»

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  • A Generalized Fault Coverage Model for Linear Time-Invariant Systems

    Page(s): 553 - 567
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    This paper proposes a fault coverage model for linear time-invariant (LTI) systems subject to uncertain input. A state-space representation, defined by the state-transition matrix, and the input matrix, is used to represent LTI system dynamic behavior. The uncertain input is considered to be unknown but bounded, where the bound is defined by an ellipsoid. The state-transition matrix, and the input matrix must be such that, for any possible input, the system dynamics meets its intended function, which can be defined by some performance requirements. These performance requirements constrain the system trajectories to some region of the state-space defined by a symmetrical polytope. When a fault occurs, the state-transition matrix, and the input matrix might be altered; and then, it is guaranteed the system survives the fault if all possible post-fault trajectories are fully contained in the region of the state-space defined by the performance requirements. This notion of guaranteed survivability is the basis to model (in the context of LTI systems) the concept of fault coverage, which is a probabilistic measure of the ability of the system to keep delivering its intended function after a fault. Analytical techniques to obtain estimates of the proposed fault coverage model are presented. To illustrate the application of the proposed model, two examples are discussed. View full abstract»

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  • Call for papers

    Page(s): 568
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  • IEEE Transactions on Reliability Information for authors

    Page(s): 569 - 570
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  • Have you visited lately? www.ieee.org [advertisement]

    Page(s): 571
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  • TR Upcoming Events

    Page(s): 572
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  • IEEE Transactions on Reliability institutional listings

    Page(s): C3
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  • IEEE Transactions on Reliability institutional listings

    Page(s): C4
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Aims & Scope

IEEE Transactions on Reliability is concerned with the problems involved in attaining reliability, maintaining it through the life of the system or device, and measuring it.

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Meet Our Editors

Editor-in-Chief
Way Kuo
City University of Hong Kong